Content
67%Weight 40%Scale 1-5Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
A well-structured, methodologically sound skill body with a clear sequenced workflow, concrete annotated examples, and properly signaled reference files. It is somewhat verbose in places and has minor inline/reference duplication.
Suggestions
Trim tutorial explanations of notation (e.g., 'x ∈ [0, 100] means x is between 0 and 100') and condense the line-by-line complete example to reduce token cost.
Replace or shorten the inline 'Language-Specific Considerations' section since language_specifics.md already covers it in depth, keeping only a pointer.
Add an explicit validation/checkpoint note (e.g., re-check widened fixpoints and confirm reported errors are not false positives before presenting findings).
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly efficient but includes tutorial-style padding such as 'x ∈ [0, 100] means x is between 0 and 100' and a lengthy line-by-line complete-example walkthrough that could be tightened. | 3 / 5 |
Actionability | Concrete code snippets illustrate each analysis operation and a full worked example with a report format is provided; minor gaps remain since the snippets show annotated inputs rather than a runnable analyzer. | 4 / 5 |
Workflow Clarity | A clear six-step sequence (parse → select domains → initialize → forward analysis → detect errors → report) is present, with error-detection as the implicit checkpoint, though explicit validation/feedback loops are not called out. | 4 / 5 |
Progressive Disclosure | Well-signaled one-level-deep references to three real files (abstract_domains.md, analysis_patterns.md, language_specifics.md) with content appropriately split, though the inline Language-Specific Considerations section partly duplicates the dedicated reference. | 4 / 5 |
Total | 15 / 20 Passed |